DBMS > Apache Impala vs. Derby vs. JaguarDB vs. YottaDB

System Properties Comparison Apache Impala vs. Derby vs. JaguarDB vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Performant, highly scalable DBMS for AI and IoT applications

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Key-value store

Secondary database models

Document store

Relational DBMS

using the Octo plugin

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

Technical documentation

impala.apache.org/impala-docs.html

db.apache.org/derby/manuals/index.html

www.jaguardb.com/support.html

yottadb.com/resources/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

DataJaguar, Inc.

YottaDB, LLC

Initial release

2013

1997

2015

2001

Current release

4.1.0, June 2022

10.17.1.0, November 2023

3.3 July 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

GPL V3.0

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++

the server part. Clients available in other languages

Server operating systems

Linux

All OS with a Java VM

Linux

Docker
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

by using the Octo plugin

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Java

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

rights management via user accounts

Users and groups based on OS-security mechanisms

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More resources

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